An Approach of Electrical Load Profile Analysis Based on Time Series Data Mining

Ying Shi,Tao Yu,Yaxiong Wu,Fusheng Li,Qianjin Liu,Hanxin Zhu

doi:10.1109/access.2020.3019698

Abstract

In the current electrical load profile analysis, considering the shortage of traditional methods on the typical load profile extraction of single consumers and the load profile feature extraction, this paper proposes an approach based on time series data mining. Firstly, this method reduces the dimension of the load profile of a single consumer based on the Piecewise Aggregate Approximation(PAA), and re-expresses the load profile of the consumer over a period based on the Symbolic Aggregate approXimation(SAX), representing the consumer’s load profile with a symbolic string to extract the typical load profile. Then, combined with the load characteristic indices and time series-based features, the typical load profiles of different consumers are clustered based on the K-means algorithm to analyze the power consumption behaviors. Finally, this paper performs a case analysis with a UCI test data set, and the results show that the proposed approach can excavate typical power consumption behaviors of consumers and improve the electrical load profile analysis efficiency and the clustering quality.

Highlights

With the development of smart grids and the construction of advanced metering infrastructure (AMI), a massive amount of fine-grained electricity consumption data has been collected, which contains a wealth of consumers’ information, such as the load profiles [1]–[4]
In view of the shortcomings of the existing research, this paper proposes an approach of electrical load profile analysis based on time series data mining
For all daily load profile of the consumer MT_166, we firstly reduce the dimension of the daily load profile through Piecewise Aggregate Approximation (PAA), and use symbolic aggregation approximation (SAX) method to transform each load profile into SAX word

Summary

INTRODUCTION

With the development of smart grids and the construction of advanced metering infrastructure (AMI), a massive amount of fine-grained electricity consumption data has been collected, which contains a wealth of consumers’ information, such as the load profiles [1]–[4]. In view of the shortcomings of the existing research, this paper proposes an approach of electrical load profile analysis based on time series data mining. Combined with the load shape indices and time series features of the TLPs of different consumers, based on the K-means algorithm, the TLPs of different consumers are clustered to analyze the power consumption behaviors of different types of consumers. An experiment shows that the proposed method can mine the typical power consumption behaviors of different consumers and improve the electrical load profile analysis efficiency and clustering quality.

TYPICAL LOAD PROFILE EXTRACTION FOR A SINGLE CONSUMER

LOAD DATA DIMENSION REDUCTION

LOAD PROFILE RE-EXPRESSION

EXPERIMENTS AND RESULTS

CONCLUSION